使用 Scala，从 Java ResultSet 创建 DataFrame 或 RDD

Question

我不想直接使用spark.read方法创建数据帧或RDD。我想从 java 结果集(有 5,000,00 条记录(形成数据帧或 RDD。如果您提供勤奋的解决方案，请表示感谢。

Answer 1

首先使用 RowFactory，我们可以创建行。其次，可以使用SQLContext.createDataFrame方法将所有行转换为数据帧。希望这对你也有帮助:)。

import java.sql.Connection
import java.sql.ResultSet
import org.apache.spark.sql.RowFactory
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.Row
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.StructType

var resultSet: ResultSet = null
val rowList = new scala.collection.mutable.MutableList[Row]
var cRow: Row = null

//Resultset is created from traditional Java JDBC.
val resultSet = DbConnection.createStatement().execute("Sql")
//Looping resultset
while (resultSet.next()) {
//adding two columns into a "Row" object
cRow = RowFactory.create(resultSet.getObject(1), resultSet.getObject(2))
//adding each rows into "List" object.
rowList += (cRow)
}
val sconf = new SparkConf
sconf.setAppName("")
sconf.setMaster("local[*]")
var sContext: SparkContext = new SparkContext(sConf)
var sqlContext: SQLContext = new SQLContext(sContext)
//creates a dataframe
DF = sqlContext.createDataFrame(sContext.parallelize(rowList ,2), getSchema())
DF.show() //show the dataframe.

def getSchema(): StructType = {
val DecimalType = DataTypes.createDecimalType(38, 10)
val schema = StructType(
StructField("COUNT", LongType, false) ::
StructField("TABLE_NAME", StringType, false) :: Nil)
//Returning the schema to define dataframe columns.
schema
}